Intrusion Detection System using Deep Neural Network and Regularization of Hyper Parameters with Adam Optimizer
Dy Director, DRC, National Intelligence Grid, MHA, Bengaluru, RAO T. CHANDRA SEKHAR, K. Thangavel · International Journal of Engineering and Advanced Technology · 2019
Intrusion Detection Systems (IDSs) study is unavoidable in the field of network security due to the present target oriented attacks for taking secret data of an organization. Classifying and detecting attacks are highly technical and tedious. In the existing models, the accuracy of intrusion detection in network traffic is different for different algorithms. This paper proposed a better intrusion detection system using Deep Neural Network with regularization of the hyper parameters. Adam optimization is proposed to optimize the weights in the neural network. The proposed system consists of six phases namely data collection, data framing, splitting of data for training and testing, pre-processing/encoding, regularization with Adam Optimizer, training and testing. It produces the better accuracy in detection process than the existing Deep Neural Network model. The bench mark data set NSL_KDD is collected and processed in the suggested system.